> For a dataset, I used 10-fold cross-validation to train a Neural Network
> model, the EER I get is around 15%. The NN model is saved.
>
> I wish to generate the predications using the saved model for the original
> whole dataset. The results is surprisingly high, EER only 1%.
>
> I have thought the saved model could only give me a result close to 15%.
>
> Could you please give any comments how to get an objective result using the
> saved model?
10-fold cross-validation (CV) generates 10 different models on
different train/test subsets, which means you can't save this as a
model. When you save a model (even though performing CV), then you
save the model that was generated on the *full* training set. A model
trained and evaluated on the same dataset is always overly optimistic
(i.e., overfitted). The reason why one normally performs 10 runs of
10-fold CV.
Cheers, Peter
--
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/ Ph. +64 (7) 858-5174
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